Mutual Information Maximization

Mutual information measures how much knowing one variable tells us about another, and maximizing it can help models retain useful signals while discarding irrelevant variation. Representation-learning methods use contrastive or multi-view objectives, entropy shaping, and variational estimators such as MINE to align features with labels, other modalities, or views of the same input. Related methods use information gain to guide data acquisition and planning, or encourage controllable diversity in generation and language-model alignment. A central challenge is estimating and optimizing mutual information in high-dimensional settings while balancing task relevance against diversity, class imbalance, and redundant features.